Authors:
Mojtaba Bandarabadi
1
;
Jalil Rasekhi
2
;
Cesar A. Teixeira
1
and
António Dourado
1
Affiliations:
1
University of Coimbra, Portugal
;
2
Babol Noshirvani University of Technology, Iran, Islamic Republic of
Keyword(s):
Epilepsy, Seizure Detection, Singular Value Decomposition, Coherency, Synchronization, Bipolar Electroencephalogram.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Detection and Identification
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
We propose a robust method for automated detection of epileptic seizures using intracranial electroencephalogram (iEEG) recordings with two electrodes. The state-of-the-art seizure detection methods suffer from high number of false detections, even when designed to be patient-specific. The solution reported here aims to achieve very low false detection rate, while providing a high sensitivity. Two adjacent iEEG recordings are subtracted from each other to make the bipolar iEEG signal. The values achieved from singular value decomposition (SVD) of the bipolar iEEG signal are used as measure. A threshold is subsequently applied on the measure. Results indicate robustness of the proposed measure for seizure detection. The method is applied on 5 invasive recordings containing 54 seizures in 780 hours of multichannel iEEG recordings. On average, the results revealed 85.2% sensitivity and a very low false detection rate of 0.02 per hour in long-term continuous iEEG recordings.